Prediction of radiation-induced acute skin toxicity in breast cancer patients using data encapsulation screening and dose-gradient-based multi-region radiomics technique: A multicenter study

Feng, Huichun; Wang, Hui; Xu, Lixia; Ren, Yanling; Ni, Qianxi; Ma, Shenglin; Deng, Qinghua; Chen, Xueqin; Xia, Bing; Kuang, Yu; Li, Xiadong

doi:10.3389/fonc.2022.1017435

Cited by 11 publications

(10 citation statements)

References 28 publications

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“…However, the GB meta-learner was the best multi-level stacking ensemble method to predict radiation-induced dermatitis 2 + with an AUC of 0.97 in the training dataset and 0.93 in the validation dataset [ 37 ]. Feng et al developed an ML tool with the highest AUC of 0.911 [95% CI 0.838–0.983] through GBDT modeling utilizing internal cross-validation to predict the incidence of radiation dermatitis ≥ 2 using radiomics features derived from multiple dose-gradient-based ROIs of patients' planning CT images, in conjunction with clinical and dosimetric parameters [ 38 ]. However, further verification is needed in another center [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

Saadatmand,

Mahdavi,

Nikoofar

et al. 2024

Eur J Med Res

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Background Radiation induced acute skin toxicity (AST) is considered as a common side effect of breast radiation therapy. The goal of this study was to design dosiomics-based machine learning (ML) models for prediction of AST, to enable creating optimized treatment plans for high-risk individuals. Methods Dosiomics features extracted using Pyradiomics tool (v3.0.1), along with treatment plan-derived dose volume histograms (DVHs), and patient-specific treatment-related (PTR) data of breast cancer patients were used for modeling. Clinical scoring was done using the Common Terminology Criteria for Adverse Events (CTCAE) V4.0 criteria for skin-specific symptoms. The 52 breast cancer patients were grouped into AST 2 + (CTCAE ≥ 2) and AST 2 − (CTCAE < 2) toxicity grades to facilitate AST modeling. They were randomly divided into training (70%) and testing (30%) cohorts. Multiple prediction models were assessed through multivariate analysis, incorporating different combinations of feature groups (dosiomics, DVH, and PTR) individually and collectively. In total, seven unique combinations, along with seven classification algorithms, were considered after feature selection. The performance of each model was evaluated on the test group using the area under the receiver operating characteristic curve (AUC) and f1-score. Accuracy, precision, and recall of each model were also studied. Statistical analysis involved features differences between AST 2 − and AST 2 + groups and cutoff value calculations. Results Results showed that 44% of the patients developed AST 2 + after Tomotherapy. The dosiomics (DOS) model, developed using dosiomics features, exhibited a noteworthy improvement in AUC (up to 0.78), when spatial information is preserved in the dose distribution, compared to DVH features (up to 0.71). Furthermore, a baseline ML model created using only PTR features for comparison with DOS models showed the significance of dosiomics in early AST prediction. By employing the Extra Tree (ET) classifiers, the DOS + DVH + PTR model achieved a statistically significant improved performance in terms of AUC (0.83; 95% CI 0.71–0.90), accuracy (0.70), precision (0.74) and sensitivity (0.72) compared to other models. Conclusions This study confirmed the benefit of dosiomics-based ML in the prediction of AST. However, the combination of dosiomics, DVH, and PTR yields significant improvement in AST prediction. The results of this study provide the opportunity for timely interventions to prevent the occurrence of radiation induced AST.

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Section: Discussionmentioning

confidence: 99%

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

Saadatmand,

Mahdavi,

Nikoofar

et al. 2024

Eur J Med Res

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“…Ionizing radiation disrupts mitosis of epidermic cells leading to worse integrity of the skin [29]. Radiodermatitis [RD] is one of the most common complications during radiation therapy for cancers including BC [30].…”

Section: Radiation Therapymentioning

confidence: 99%

“…RD manifests as erythema, pigmentary changes, ulceration, itching, soreness, or peeling skin [33]. Moreover, RD can lead to dose reduction or interruption therapy, but also esthetic, psychological problems and affect negatively the daily functioning and quality of life [29,30]. Developing RD is more probable among obese, older patients, females, and smokers [30].…”

Section: Radiation Therapymentioning

confidence: 99%

“…In addition, drinking, sun exposure, other taken drugs play a role in dermatitis. Identifying risk factors of RD, early prediction of skin reaction, and early management is a way to improve the comfort of patients and prevention of deeper skin injuries [29]. Feng et al proved data encapsulation screening and multi-region dose-gradient-based radiomics techniques have potential to be used in the prediction of skin toxicity induced by radiation in BC patients [29].…”

Section: Radiation Therapymentioning

confidence: 99%

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Adverse effects in the management of breast cancer – recent studies

Maksymowicz,

Machowiec,

Korzeniowska

et al. 2023

J Educ Health Sport

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Introduction and purpose Breast cancer (BC) management includes local therapies surgery, radiotherapy and systemic – hormonotherapy, chemotherapy, targeted and immunotherapy. However, some adverse events of these treatment strategies limit their wide administration in BC therapy. The aim of the study was to present adverse effects in the management of BC, prevention and treatment of them. State of the knowledge Surgery of the breast leads to lymphedema, pain, and anatomical changes. The most characteristic adverse event of radiotherapy is radiation dermatitis which is more probable among obese, older patients, females, and smokers. Other side effects are pneumonia, cardiac and pulmonary injury. Chemotherapeutics lead to complications such as anthracyclines-induced cardiotoxicity. As a consequence of endocrine therapy, patients are affected by vasomotor, musculoskeletal, and vulvovaginal symptoms. Immune checkpoint inhibitors may cause immune-related adverse events (irAEs), which are usually mild. When severe irAEs occur, immunosuppressive drugs are used. Depending on stage, toxicities of the therapies can lead to interrupting the treatment of BC. Moreover, drug resistance is an important therapeutic obstacle in BC treatment. Conclusion The application of a diversity of drugs in the treatment of breast cancer is associated with adverse effects which limit its efficacy. It is vital to develop novel, targeted therapeutic methods to optimize outcomes of patients. Given the adverse effects of breast cancer therapies and drug resistance, it is vital to develop novel, targeted therapeutic methods.

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“…After optimization, the random forest algorithm was found the best model, able to classify patients with acceptable performance in the validation cohort (AUC = 0.77). Feng et al (15) developed a novel quantitative ML tool for prediction of grade ≥ 2 dermatitis before radiotherapy by using data encapsulation screening and multi-region dose-gradient-based radiomics techniques, in addition to clinical and dosimetric parameters. Using data of 214 patients, a combination of 20 radiomics features and 8 clinical and dosimetric variable achieve an AUC of 0.911 in the validation dataset.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry

et al. 2023

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PurposeRadiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity.Methods and materialsOne hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (IM) and erythema (IE) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4-time intervals: before RT, at the end of RT and 1 and 6 months after the end of RT. Together with clinical covariates, melanin and erythema indices were correlated with skin toxicity, evaluated using the Radiation Therapy Oncology Group (RTOG) guidelines. Binary group classes were labeled according to a RTOG cut-off score of ≥ 2. The patient’s dataset was randomly split into a training and testing set used for model development/validation and testing (75%/25% split). A 5-times repeated holdout cross-validation was performed. Three supervised machine learning models, including support vector machine (SVM), classification and regression tree analysis (CART) and logistic regression (LR), were employed for modeling and skin toxicity prediction purposes.ResultsThirty-four (26.4%) patients presented with adverse skin effects (RTOG ≥2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (IM,T0 and IE,T0), together with the volumes of breast (PTV2) and boost surgical cavity (PTV1), the body mass index (BMI) and the dose fractionation scheme (FRAC) were found significantly associated with the RTOG score groups (p<0.05) in univariate analysis. The diagnostic performances measured by the area-under-curve (AUC) were 0.816, 0.734, 0.714, 0.691 and 0.664 for IM, IE, PTV2, PTV1 and BMI, respectively. Classification performances reported precision, recall and F1-values greater than 0.8 for all models. The SVM classifier using the RBF kernel had the best performance, with accuracy, precision, recall and F-score equal to 89.8%, 88.7%, 98.6% and 93.3%, respectively. CART analysis classified patients with IM,T0 ≥ 99 to be associated with RTOG ≥ 2 toxicity; subsequently, PTV1 and PTV2 played a significant role in increasing the classification rate. The CART model provided a very high diagnostic performance of AUC=0.959.ConclusionsSpectrophotometry is an objective and reliable tool able to assess radiation induced skin tissue injury. Using a machine learning approach, we were able to predict grade RTOG ≥2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life.

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Prediction of radiation-induced acute skin toxicity in breast cancer patients using data encapsulation screening and dose-gradient-based multi-region radiomics technique: A multicenter study

Cited by 11 publications

References 28 publications

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

Adverse effects in the management of breast cancer – recent studies

Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry

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